ReviewComputational and structural biotechnology journal2020
Computational approaches for effective CRISPR guide RNA design and evaluation.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
105 citing papers in PubMed.
- From RNA design to delivery: Computational strategies for functional RNA therapeutics.Bioactive materials · 2027Review
- Machine Learning for CRISPR-Based Diagnostics.International journal of molecular sciences · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Expanding the Microbial Genomic Landscape and Biotechnological Applications of CRISPR-Cas Systems.Biology · 2026Review
- Allele-selective disruption of pathogenic VWF variants in type 2 von Willebrand disease using CRISPR/Cas9.Blood advances · 2026Article
- Recent developments in CRISPR/Cas9 genome editing research for edible fungiculture.Functional & integrative genomics · 2026Review
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- Article
- Rational Targeting and gRNA Design for Enhancing Quorum Quenching inComputational and structural biotechnology journal · 2026Article
- Advances in gene editing for legume improvement: technologies, progress, and prospects.Frontiers in genome editing · 2026Review
- A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.Frontiers in plant science · 2026Review
- Applications of Genome-wide Screening of CRISPR/Cas9 Knockout Library in Identifying Novel Molecular Targets for Xenobiotics.Environment & health (Washington, D.C.) · 2025Review
- NDR2 Kinase Regulates Microglial Metabolic Adaptation and Inflammatory Response: Critical Role in Glucose-Dependent Functional Plasticity.International journal of molecular sciences · 2025Article
- Engineering adeno-associated viral vectors for CRISPR/Cas based in vivo therapeutic genome editing.Biomaterials · 2025Review
- The CRISPR-Cas9 System inMicroorganisms · 2025Article
- Off-target effects in CRISPR-Cas genome editing for human therapeutics: Progress and challenges.Molecular therapy. Nucleic acids · 2025Review
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- mRNA folding algorithms for structure and codon optimization.Briefings in bioinformatics · 2025Review
- Deep Learning Based Models for CRISPR/Cas Off-Target Prediction.Small methods · 2025Review
- Highly Efficient Generation of Germline Mutations Using CRISPR/Cas9 in the Speckled Wood ButterflyEcology and evolution · 2025Article
45 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)/ CRISPR-associated (Cas) system has emerged as the main technology for gene editing. Successful editing by CRISPR requires an appropriate Cas protein and guide RNA. However, low cleavage efficiency and off-target effects hamper the development and application of CRISPR/Cas systems. To predict cleavage efficiency and specificity, numerous computational approaches have been developed for scoring guide RNAs. Most scores are empirical or trained by experimental datasets, and scores are implemented using various computational methods. Herein, we discuss these approaches, focusing mainly on the features or computational methods they utilise. Furthermore, we summarise these tools and give some suggestions for their usage. We also recommend three versatile web-based tools with user-friendly interfaces and preferable functions. The review provides a comprehensive and up-to-date overview of computational approaches for guide RNA design that could help users to select the optimal tools for their research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.